As used herein, the phrases “spectral synthesis” and “spectral synthesis for image capture device processing” may relate to processing methods that may be performed or computed to achieve accurate color output, e.g., from image capture devices. Tristimulus color processing models, such as RGB (red, green, blue), are commonplace. While RGB and other tristimulus models suffice for color identification, matching, and classification, such models may be inherently limited in relation to color processing. By its nature, light comprises a spectrum of electromagnetic energy, which generally cannot be represented completely by, for instance, a red, a green, and a blue color value. With RGB based information as well as tristimulus values corresponding to cone cells receptive to short, medium, and long wavelength light (e.g., blue, green, and red), the human visual system (HVS) attempts to infer an original, natural stimulus.
Multi-spectral systems typically capture, process, and display multi-spectral images. Multi-spectral cameras for example may output more than three channels. Output channels can be rendered with a multi-primary printer or display. Some multi-spectral systems are designed to render a print output with a reflectance spectrum that is nearly identical to a reflectance spectrum of an original object. Multi-spectral representations of images generally fall into two classes. The more common class measures intensity or reflectance over smaller intervals in wavelength, which generally necessitates use of more than three channels (e.g., more than channels R, G, and B) (see reference [1], incorporated herein by reference in its entirety). The less common class uses the Wyszecki hypothesis (see reference [2], incorporated herein by reference in its entirety) which characterizes reflectance spectra as being comprised of two components, a fundamental component which captures a perceptually relevant tristimulus representation plus a residual component which represents the gross features of the overall reflectance spectrum. Wyszecki labeled this residual component the metameric black. An example of this second class is the LabPQR color space. In the LabPQR representation, the tristimulus portion is the Lab color space while PQR represents the residual. For emissive rendering and presentation of images using electronic displays, reflectance spectra identity is not crucial.
A picture produced by a camera or other image capture device is generally not quite the same as what would be perceived by human eyes.
Processing inside an image capture device generally involves a 3×3 matrix that transforms sensor outputs into a color space of an output image. Results of applying this matrix transformation generally do not reproduce what would be perceived by human eyes unless spectral sensitivities of the image capture device's sensors can be represented as a linear combination of color matching functions. In many cases, magnitude of these errors in the results is not inconsequential.
Existing DSLR (digital single-lens reflex) cameras, for instance, may have a knob to select a different 3×3 matrix for different types of scenes (e.g., night, sports, cloudy, portrait, etc.). However, in practice, getting the color right in general and also, for instance, for certain memory colors, such as face (skin) tones, can be problematic.